The present invention relates generally to an analysis of train operational data from a train during a run, and more specifically, to the processing of the data and making it available for further analysis.
Operational data is usually collected during a run on the train. This has been analyzed using various methods and produced information in strip charts and other various forms of reports. The information is then stored and accessed individually and analyzed. Historically, there has been only limited ability to search the information based on given criteria for further analysis or reports. To make operational analysis even more difficult, railroad imposed operating constraints will vary day to day based on work crews, track conditions, etc. An operational manager may desire to analyze train runs that were exposed to specific operating constraints to ensure rules compliance and optimal performance. Since such constraints vary day to day, the manager needs an efficient way to define the constraints and sift through a large amount of data to determine rules compliance and performance level.
The present invention provides a method of preprocessing the data to allow it to be searched and further analyzed to an arbitrary user designed criteria.
The present invention is a method of analyzing train operational data collected onboard a train. The train operational data is recorded during each run of a train and transferred to a processing station. Operational and informational parameters are derived from the recorded operational data for each run. The operational and identification parameters and the corresponding operational data are stored as standard database records, one for each run in a database. The operational parameters are compared to selected exception values and the variance of the comparison are stored with the standard operational database record for each run. A search can then be performed of the stored standard database records based on one or more of operational parameter, identification parameter, operational data and variance, and the results are provided. The processing of the operational data and creation of a standard database allow for a most efficient search and analysis.
Preferably, each standard database record is sorted into segments related to an identification parameter and stored. The identification parameter includes one or more geographic locations, geographic features, crew, crew activities, operational constraints, train type, and consist. The operational parameters may include one or more of fuel consumption, in-train forces, time to destination, speed limit adherence, air brake preference and dynamic brake preference. The process further includes tuning variables of the derivation using the recorded operational data and storing the operational and identification parameters after the tuning. The variables to be tuned may include one or more of curve resistance efficiency, weight of the train, rolling resistance efficiency, air brake efficiency, dynamic brake efficiency, propulsion system efficiency and number of retainer sets. The tuned variables are stored with the standard data and with the corresponding parameters and data.
The search criteria is displayed with a first list of database records which meet the search criteria and a second list of one or more database records selected from those that meet the criteria and are to be processed. Also, a first list of operational parameters which can be compared are displayed with a second list of selected parameters to be prepared with their exception values.
Operational constraints of the run may be inputted and used to derive an optimal performance prediction of the run under analysis. The derivation includes deriving operational and identification parameters from the recorded data and the operational constraints. The operational constraints are stored in the standard database with the corresponding parameters and data. A target database of preferred operation data from the recorded operational data based on defined goals is derived. The target database with the standard database of the run is stored as a record. The defined goals may include weighting of operational parameters and limits for operational data. The operational parameters to be weighted include one or more of fuel consumption, in-train forces, time to destination, speed limit adherence, air brake preference and dynamic brake preference. The limits for operational data include one or more of steady state forces, slack action, speed limit and stall speed.
The initial processing to create the standard database record is preferably performed automatically upon receipt of operational data for a new run. Also, the transfer of the operational data is preferably automatic at the end of a run. It may also be performed on a playback system.